proposals | R Documentation |
Functions to construct proposal distributions for use with MCMC methods.
mvn.diag.rw(rw.sd) mvn.rw(rw.var) mvn.rw.adaptive( rw.sd, rw.var, scale.start = NA, scale.cooling = 0.999, shape.start = NA, target = 0.234, max.scaling = 50 )
rw.sd |
named numeric vector; random-walk SDs for a multivariate normal random-walk proposal with diagonal variance-covariance matrix. |
rw.var |
square numeric matrix with row- and column-names. Specifies the variance-covariance matrix for a multivariate normal random-walk proposal distribution. |
scale.start, scale.cooling, shape.start, target, max.scaling |
parameters
to control the proposal adaptation algorithm. Beginning with MCMC
iteration |
Each of these calls constructs a function suitable for use as the
proposal
argument of pmcmc
or abc
. Given a parameter
vector, each such function returns a single draw from the corresponding
proposal distribution.
Aaron A. King, Sebastian Funk
2009
More on Markov chain Monte Carlo methods:
approximate Bayesian computation
,
pmcmc()
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